Instructions to use featherless-ai-quants/google-codegemma-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use featherless-ai-quants/google-codegemma-2b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="featherless-ai-quants/google-codegemma-2b-GGUF",
	filename="google-codegemma-2b-IQ4_XS.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use featherless-ai-quants/google-codegemma-2b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use featherless-ai-quants/google-codegemma-2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "featherless-ai-quants/google-codegemma-2b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "featherless-ai-quants/google-codegemma-2b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M

Ollama
How to use featherless-ai-quants/google-codegemma-2b-GGUF with Ollama:
```
ollama run hf.co/featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M
```

Unsloth Studio

How to use featherless-ai-quants/google-codegemma-2b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for featherless-ai-quants/google-codegemma-2b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for featherless-ai-quants/google-codegemma-2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for featherless-ai-quants/google-codegemma-2b-GGUF to start chatting

Docker Model Runner
How to use featherless-ai-quants/google-codegemma-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M
```

Lemonade

How to use featherless-ai-quants/google-codegemma-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull featherless-ai-quants/google-codegemma-2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.google-codegemma-2b-GGUF-Q4_K_M

List all available models

lemonade list

google/codegemma-2b GGUF Quantizations 🚀

Optimized GGUF quantization files for enhanced model performance

Powered by Featherless AI - run any model you'd like for a simple small fee.

Available Quantizations 📊

Quantization Type	File	Size
IQ4_XS	google-codegemma-2b-IQ4_XS.gguf	1431.67 MB
Q2_K	google-codegemma-2b-Q2_K.gguf	1104.28 MB
Q3_K_L	google-codegemma-2b-Q3_K_L.gguf	1397.70 MB
Q3_K_M	google-codegemma-2b-Q3_K_M.gguf	1319.70 MB
Q3_K_S	google-codegemma-2b-Q3_K_S.gguf	1228.31 MB
Q4_K_M	google-codegemma-2b-Q4_K_M.gguf	1554.74 MB
Q4_K_S	google-codegemma-2b-Q4_K_S.gguf	1487.58 MB
Q5_K_M	google-codegemma-2b-Q5_K_M.gguf	1754.43 MB
Q5_K_S	google-codegemma-2b-Q5_K_S.gguf	1715.58 MB
Q6_K	google-codegemma-2b-Q6_K.gguf	1966.59 MB
Q8_0	google-codegemma-2b-Q8_0.gguf	2545.42 MB

⚡ Powered by Featherless AI

Key Features

🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly
🛠️ Zero Infrastructure - No server setup or maintenance required
📚 Vast Compatibility - Support for 2400+ models and counting
💎 Affordable Pricing - Starting at just $10/month

Links:
Get Started | Documentation | Models

Downloads last month: 259

GGUF

Model size

3B params

Architecture

gemma

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for featherless-ai-quants/google-codegemma-2b-GGUF

Base model

google/codegemma-2b

Quantized

(11)

this model